Cross-efficiency analysis of energy sector using stochastic DEA: Considering pollutant emissions
Publication date
2024-07-01Rights
© 2024 Elsevier. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license (http://creativecommons.org/licenses/by-nc-nd/4.0/)Peer-Reviewed
YesOpen Access status
embargoedAccessAccepted for publication
2024-05-30
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Show full item recordAbstract
Undesirable outputs can be challenging to avoid in the production of goods and services, often overlooked. Pollution is generally regarded as a negative externality and is taken into account during the production process. The novelty of this study lies in introducing CO2 as an economic “bad” in the energy sector's efficiency measure through a stochastic data envelopment analysis (DEA) cross-efficiency model. Unlike pollution and economic goods, where increased production leads to more pollution, CO2 is weakly disposable, meaning that higher CO2 values lead to a decrease in the number of good outputs produced. The study proposes a new stochastic model based on an extension of the cross-efficiency model and applies it to measure the energy efficiency of 32 thermal power plants in Angola in the presence of undesirable outputs. This will help promote better environmental management. The study's findings offer vital policy insights for the energy sector. The introduction of new stochastic models enables more accurate efficiency measurement under uncertain conditions, aiding policymakers in resource allocation decisions. Additionally, the adoption of stochastic cross-efficiency methods enhances performance assessments, facilitating targeted interventions for underperforming units. These findings contribute to evidence-based policymaking, promoting sustainability and competitiveness within the energy sector.Version
Accepted manuscriptCitation
Hadi-Vencheh A, Kohadadadipour M, Tan Y et al (2024) Cross-efficiency analysis of energy sector using stochastic DEA: Considering pollutant emissions. Journal of Environmental Management. 364: 121319.Link to Version of Record
https://doi.org/10.1016/j.jenvman.2024.121319Type
Articleae974a485f413a2113503eed53cd6c53
https://doi.org/10.1016/j.jenvman.2024.121319